The Problem: Data Visualization is a Pain

You’ve got your data neatly organized in DuckDB. Your boss wants a dashboard — yesterday.

Your options:

• Metabase → Free but heavy. Need a separate server, Java runtime, lots of config.
• Python + Plotly → 200+ lines of code for what should be a 5-line query.
• Excel charts → 1990 called, they want their workflow back.

What if you could just write SQL and get a chart?

Shaper is exactly that — an open-source, SQL-driven dashboard tool powered by DuckDB under the hood. Write SQL queries with type annotations, and Shaper renders them as bar charts, line charts, pie charts, tables, and more.

GitHub: https://github.com/taleshape-com/shaper (1.1k ⭐, actively maintained)

What is Shaper?

Shaper’s positioning is crystal clear: the DuckDB visualization layer for SQL users.

“As Official says: All in SQL, Powered by DuckDB.”

The core idea: You don’t need to learn any new API or DSL. Just append ::BARCHART, ::XAXIS, etc. to your SQL columns and Shaper figures out how to visualize them.

-- Shaper SQL example
SELECT
 date_trunc('week', created_at)::XAXIS,
 category::CATEGORY,
 count()::BARCHART_STACKED
FROM dataset
GROUP BY ALL ORDER BY ALL;

This SQL produces a stacked bar chart — no JavaScript, no JSON config, no drag-and-drop.

Key Features

10-Minute Quickstart

Minute 1: Start Shaper

The fastest way to try Shaper is via Docker:

docker run --rm -it -p5454:5454 taleshape/shaper

Open http://localhost:5454 — you’ll see a clean dashboard editor.

No Docker? Shaper also ships as npm and pip packages:

• npm install @taleshape/shaper
• pip install shaper

Minutes 2-5: Import Data + First Query

Click “New Query” and write your first SQL:

-- See what we're working with
SELECT * FROM read_csv_auto('sales_2024.csv') LIMIT 10;

Shaper uses DuckDB’s engine natively, so all DuckDB features work — read_csv_auto, read_parquet, ATTACH for MySQL/PostgreSQL…

Now for a real chart:

SELECT
 strftime(order_date, '%Y-%m')::XAXIS,
 product_category::CATEGORY,
 SUM(amount)::BARCHART_STACKED
FROM read_csv_auto('sales_2024.csv')
GROUP BY ALL ORDER BY ALL;

Minutes 6-10: Assemble Your Dashboard

Add more queries to build a complete dashboard:

KPI Card:

SELECT
 'Total Revenue'::LABEL,
 '$' || FORMAT('%,.0f', SUM(amount))::VALUE,
 'vs last month ' || CASE WHEN SUM(amount) - LAG(SUM(amount)) OVER () > 0 THEN '↑' ELSE '↓' END || FORMAT('%.1f%%', ABS((SUM(amount) - LAG(SUM(amount)) OVER ()) / LAG(SUM(amount)) OVER () * 100))::SUBTITLE
FROM read_csv_auto('sales_2024.csv');

Trend Line:

SELECT
 order_date::XAXIS,
 SUM(amount)::LINE
FROM read_csv_auto('sales_2024.csv')
GROUP BY ALL ORDER BY ALL;

Top 10 Products:

SELECT
 product_name::LABEL,
 SUM(amount)::BARCHART
FROM read_csv_auto('sales_2024.csv')
GROUP BY ALL ORDER BY SUM(amount) DESC LIMIT 10;

Ten minutes. A 4-component professional dashboard. All SQL. No frontend code.

Advanced Features

4.1 KPI Monitoring & Alerts

Shaper supports scheduled scans that trigger alerts when metrics cross thresholds:

-- Alert if today's sales < $10,000
SELECT
 SUM(amount)::VALUE,
 CASE WHEN SUM(amount) < 10000 THEN '🚨 Below Target'
 ELSE '✅ On Track' END::STATUS
FROM read_csv_auto('sales_2024.csv')
WHERE order_date = CURRENT_DATE;

4.2 Embedded Dashboards

Want to embed dashboards in your product? Shaper supports:

• iframe embedding: One line of HTML
• JS/React SDK: Full control over styling, no iframe needed
• White-label mode: Hide Shaper branding

<!-- iframe embed -->
<iframe src="https://your-shaper.com/d/sales-dashboard"
 width="100%" height="600px"
 frameborder="0"></iframe>

// React SDK approach
import { ShaperDashboard } from '@taleshape/shaper-react';

function App() {
 return <ShaperDashboard dashboardId="sales-dashboard"
 token="your-jwt-token" />;
}

4.3 Scheduled PDF Reports

Configure a cron job to generate PDF reports on schedule:

# Export via CLI
curl -X POST https://your-shaper.com/api/dashboards/sales-weekly/export \
 -H "Authorization: Bearer your-token" \
 -d '{"format": "pdf"}'

When Should You Use Shaper?

Best for:

• Teams already using DuckDB for data analysis
• SQL analysts who need quick charts
• Developers building client-facing dashboards
• Backend engineers who want to avoid JavaScript

Not ideal for:

• Complex interactive dashboards with drill-down animations
• Non-technical users who need drag-and-drop (Shaper is SQL-driven)

Bottom line: If you already use DuckDB, Shaper is the shortest path from query to dashboard to share.

Summary

Shaper fills the missing visualization layer in the DuckDB ecosystem. It bridges the gap between “querying data with SQL” and “seeing charts that drive decisions” — no new tools, no frontend code, just the SQL you already know.

10 minutes. Zero to a professional dashboard. That’s Shaper’s promise.

Get started now:

docker run --rm -it -p5454:5454 taleshape/shaper

Open http://localhost:5454 and give it a try.

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如发现错误，欢迎通过 GitHub Issue 或邮件 contact@duckdblab.org 反馈。